Dynamic selection of models for hybrid network assurance architectures

ABSTRACT

In one embodiment, a local service of a network reports configuration information regarding the network to a cloud-based network assurance service. The local service receives a classifier selected by the cloud-based network assurance service based on the configuration information regarding the network. The local service classifies, using the received classifier, telemetry data collected from the network, to select a modeling strategy for the network. The local service installs, based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to the dynamic selection of models for network assurancearchitectures.

BACKGROUND

Networks are large-scale distributed systems governed by complexdynamics and very large number of parameters. In general, networkassurance involves applying analytics to captured network information,to assess the health of the network. For example, a network assurancesystem may track and assess metrics such as available bandwidth, packetloss, jitter, and the like, to ensure that the experiences of users ofthe network are not impinged. However, as networks continue to evolve,so too will the number of applications present in a given network, aswell as the number of metrics available from the network.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example communication network;

FIG. 2 illustrates an example network device/node;

FIG. 3 illustrates an example network assurance system;

FIGS. 4A-4F illustrate an example hybrid network assurance system; and

FIG. 5 illustrates an example simplified procedure for model selectionin a hybrid network assurance system.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a local serviceof a network reports configuration information regarding the network toa cloud-based network assurance service. The local service receives aclassifier selected by the cloud-based network assurance service basedon the configuration information regarding the network. The localservice classifies, using the received classifier, telemetry datacollected from the network, to select a modeling strategy for thenetwork. The local service installs, based on the modeling strategy forthe network, a machine learning-based model to the local service formonitoring the network.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,with the types ranging from local area networks (LANs) to wide areanetworks (WANs). LANs typically connect the nodes over dedicated privatecommunications links located in the same general physical location, suchas a building or campus. WANs, on the other hand, typically connectgeographically dispersed nodes over long-distance communications links,such as common carrier telephone lines, optical lightpaths, synchronousoptical networks (SONET), or synchronous digital hierarchy (SDH) links,or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, andothers. The Internet is an example of a WAN that connects disparatenetworks throughout the world, providing global communication betweennodes on various networks. The nodes typically communicate over thenetwork by exchanging discrete frames or packets of data according topredefined protocols, such as the Transmission Control Protocol/InternetProtocol (TCP/IP). In this context, a protocol consists of a set ofrules defining how the nodes interact with each other. Computer networksmay be further interconnected by an intermediate network node, such as arouter, to extend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or PLC networks. That is, in addition to one or more sensors,each sensor device (node) in a sensor network may generally be equippedwith a radio transceiver or other communication port such as PLC, amicrocontroller, and an energy source, such as a battery. Often, smartobject networks are considered field area networks (FANs), neighborhoodarea networks (NANs), personal area networks (PANs), etc. Generally,size and cost constraints on smart object nodes (e.g., sensors) resultin corresponding constraints on resources such as energy, memory,computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics. For the sake ofillustration, a given customer site may fall under any of the followingcategories:

1.) Site Type A: a site connected to the network (e.g., via a private orVPN link) using a single CE router and a single link, with potentially abackup link (e.g., a 3G/4G/LTE backup connection). For example, aparticular CE router 110 shown in network 100 may support a givencustomer site, potentially also with a backup link, such as a wirelessconnection.

2.) Site Type B: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection). A site of type B may itselfbe of different types:

2a.) Site Type B1: a site connected to the network using two MPLS VPNlinks (e.g., from different Service Providers), with potentially abackup link (e.g., a 3G/4G/LTE connection).

2b.) Site Type B2: a site connected to the network using one MPLS VPNlink and one link connected to the public Internet, with potentially abackup link (e.g., a 3G/4G/LTE connection). For example, a particularcustomer site may be connected to network 100 via PE-3 and via aseparate Internet connection, potentially also with a wireless backuplink.

2c.) Site Type B3: a site connected to the network using two linksconnected to the public Internet, with potentially a backup link (e.g.,a 3G/4G/LTE connection).

Notably, MPLS VPN links are usually tied to a committed service levelagreement, whereas Internet links may either have no service levelagreement at all or a loose service level agreement (e.g., a “GoldPackage” Internet service connection that guarantees a certain level ofperformance to a customer site).

3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but withmore than one CE router (e.g., a first CE router connected to one linkwhile a second CE router is connected to the other link), andpotentially a backup link (e.g., a wireless 3G/4G/LTE backup link). Forexample, a particular customer site may include a first CE router 110connected to PE-2 and a second CE router 110 connected to PE-3.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations.

Servers 152-154 may include, in various embodiments, a networkmanagement server (NMS), a dynamic host configuration protocol (DHCP)server, a constrained application protocol (CoAP) server, an outagemanagement system (OMS), an application policy infrastructure controller(APIC), an application server, etc. As would be appreciated, network 100may include any number of local networks, data centers, cloudenvironments, devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc.

In various embodiments, network 100 may include one or more meshnetworks, such as an Internet of Things network. Loosely, the term“Internet of Things” or “IoT” refers to uniquely identifiable objects(things) and their virtual representations in a network-basedarchitecture. In particular, the next frontier in the evolution of theInternet is the ability to connect more than just computers andcommunications devices, but rather the ability to connect “objects” ingeneral, such as lights, appliances, vehicles, heating, ventilating, andair-conditioning (HVAC), windows and window shades and blinds, doors,locks, etc. The “Internet of Things” thus generally refers to theinterconnection of objects (e.g., smart objects), such as sensors andactuators, over a computer network (e.g., via IP), which may be thepublic Internet or a private network.

Notably, shared-media mesh networks, such as wireless or PLC networks,etc., are often on what is referred to as Low-Power and Lossy Networks(LLNs), which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communicationchallenges. First, LLNs communicate over a physical medium that isstrongly affected by environmental conditions that change over time.Some examples include temporal changes in interference (e.g., otherwireless networks or electrical appliances), physical obstructions(e.g., doors opening/closing, seasonal changes such as the foliagedensity of trees, etc.), and propagation characteristics of the physicalmedia (e.g., temperature or humidity changes, etc.). The time scales ofsuch temporal changes can range between milliseconds (e.g.,transmissions from other transceivers) to months (e.g., seasonal changesof an outdoor environment). In addition, LLN devices typically uselow-cost and low-power designs that limit the capabilities of theirtransceivers. In particular, LLN transceivers typically provide lowthroughput. Furthermore, LLN transceivers typically support limited linkmargin, making the effects of interference and environmental changesvisible to link and network protocols. The high number of nodes in LLNsin comparison to traditional networks also makes routing, quality ofservice (QoS), security, network management, and traffic engineeringextremely challenging, to mention a few.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the computing devices shown in FIGS. 1A-1B, particularly the PErouters 120, CE routers 110, nodes/device 10-20, servers 152-154 (e.g.,a network controller located in a data center, etc.), any othercomputing device that supports the operations of network 100 (e.g.,switches, etc.), or any of the other devices referenced below. Thedevice 200 may also be any other suitable type of device depending uponthe type of network architecture in place, such as IoT nodes, etc.Device 200 comprises one or more network interfaces 210, one or moreprocessors 220, and a memory 240 interconnected by a system bus 250, andis powered by a power supply 260.

The network interfaces 210 include the mechanical, electrical, andsignaling circuitry for communicating data over physical links coupledto the network 100. The network interfaces may be configured to transmitand/or receive data using a variety of different communicationprotocols. Notably, a physical network interface 210 may also be used toimplement one or more virtual network interfaces, such as for virtualprivate network (VPN) access, known to those skilled in the art.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor(s) 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise necessaryelements or logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242 (e.g., theInternetworking Operating System, or IOS®, of Cisco Systems, Inc.,another operating system, etc.), portions of which are typicallyresident in memory 240 and executed by the processor(s), functionallyorganizes the node by, inter alia, invoking network operations insupport of software processors and/or services executing on the device.These software processors and/or services may comprise a networkassurance process 248, as described herein, any of which mayalternatively be located within individual network interfaces.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while processes may be shown and/or describedseparately, those skilled in the art will appreciate that processes maybe routines or modules within other processes.

Network assurance process 248 includes computer executable instructionsthat, when executed by processor(s) 220, cause device 200 to performnetwork assurance functions as part of a network assuranceinfrastructure within the network. In general, network assurance refersto the branch of networking concerned with ensuring that the networkprovides an acceptable level of quality in terms of the user experience.For example, in the case of a user participating in a videoconference,the infrastructure may enforce one or more network policies regardingthe videoconference traffic, as well as monitor the state of thenetwork, to ensure that the user does not perceive potential issues inthe network (e.g., the video seen by the user freezes, the audio outputdrops, etc.).

In some embodiments, network assurance process 248 may use any number ofpredefined health status rules, to enforce policies and to monitor thehealth of the network, in view of the observed conditions of thenetwork. For example, one rule may be related to maintaining the serviceusage peak on a weekly and/or daily basis and specify that if themonitored usage variable exceeds more than 10% of the per day peak fromthe current week AND more than 10% of the last four weekly peaks, aninsight alert should be triggered and sent to a user interface.

Another example of a health status rule may involve client transitionevents in a wireless network. In such cases, whenever there is a failurein any of the transition events, the wireless controller may send areason_code to the assurance system. To evaluate a rule regarding theseconditions, the network assurance system may then group 150 failuresinto different “buckets” (e.g., Association, Authentication, Mobility,DHCP, WebAuth, Configuration, Infra, Delete, De-Authorization) andcontinue to increment these counters per service set identifier (SSID),while performing averaging every five minutes and hourly. The system mayalso maintain a client association request count per SSID every fiveminutes and hourly, as well. To trigger the rule, the system mayevaluate whether the error count in any bucket has exceeded 20% of thetotal client association request count for one hour.

In various embodiments, network assurance process 248 may also utilizemachine learning techniques, to enforce policies and to monitor thehealth of the network. In general, machine learning is concerned withthe design and the development of techniques that take as inputempirical data (such as network statistics and performance indicators),and recognize complex patterns in these data. One very common patternamong machine learning techniques is the use of an underlying model M,whose parameters are optimized for minimizing the cost functionassociated to M, given the input data. For instance, in the context ofclassification, the model M may be a straight line that separates thedata into two classes (e.g., labels) such that M=a*x+b*y+c and the costfunction would be the number of misclassified points. The learningprocess then operates by adjusting the parameters a, b, c such that thenumber of misclassified points is minimal. After this optimization phase(or learning phase), the model M can be used very easily to classify newdata points. Often, M is a statistical model, and the cost function isinversely proportional to the likelihood of M, given the input data.

In various embodiments, network assurance process 248 may employ one ormore supervised, unsupervised, or semi-supervised machine learningmodels. Generally, supervised learning entails the use of a training setof data, as noted above, that is used to train the model to apply labelsto the input data. For example, the training data may include samplenetwork observations that do, or do not, violate a given network healthstatus rule and are labeled as such. On the other end of the spectrumare unsupervised techniques that do not require a training set oflabels. Notably, while a supervised learning model may look forpreviously seen patterns that have been labeled as such, an unsupervisedmodel may instead look to whether there are sudden changes in thebehavior. Semi-supervised learning models take a middle ground approachthat uses a greatly reduced set of labeled training data.

Example machine learning techniques that network assurance process 248can employ may include, but are not limited to, nearest neighbor (NN)techniques (e.g., k-NN models, replicator NN models, etc.), statisticaltechniques (e.g., Bayesian networks, etc.), clustering techniques (e.g.,k-means, mean-shift, etc.), neural networks (e.g., reservoir networks,artificial neural networks, etc.), support vector machines (SVMs),logistic or other regression, Markov models or chains, principalcomponent analysis (PCA) (e.g., for linear models), multi-layerperceptron (MLP) ANNs (e.g., for non-linear models), replicatingreservoir networks (e.g., for non-linear models, typically for timeseries), random forest classification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of times the modelincorrectly predicted whether a network health status rule was violated.Conversely, the false negatives of the model may refer to the number oftimes the model predicted that a health status rule was not violatedwhen, in fact, the rule was violated. True negatives and positives mayrefer to the number of times the model correctly predicted whether arule was violated or not violated, respectively. Related to thesemeasurements are the concepts of recall and precision. Generally, recallrefers to the ratio of true positives to the sum of true positives andfalse negatives, which quantifies the sensitivity of the model.Similarly, precision refers to the ratio of true positives the sum oftrue and false positives.

FIG. 3 illustrates an example network assurance system 300, according tovarious embodiments. As shown, at the core of network assurance system300 may be a cloud service 302 that leverages machine learning insupport of cognitive analytics for the network, predictive analytics(e.g., models used to predict user experience, etc.), troubleshootingwith root cause analysis, and/or trending analysis for capacityplanning. Generally, architecture 300 may support both wireless andwired network, as well as LLNs/IoT networks.

In various embodiments, cloud service 302 may oversee the operations ofthe network of an entity (e.g., a company, school, etc.) that includesany number of local networks. For example, cloud service 302 may overseethe operations of the local networks of any number of branch offices(e.g., branch office 306) and/or campuses (e.g., campus 308) that may beassociated with the entity. Data collection from the various localnetworks/locations may be performed by a network data collectionplatform 304 that communicates with both cloud service 302 and themonitored network of the entity.

The network of branch office 306 may include any number of wirelessaccess points 320 (e.g., a first access point AP1 through nth accesspoint, APn) through which endpoint nodes may connect. Access points 320may, in turn, be in communication with any number of wireless LANcontrollers (WLCs) 326 (e.g., supervisory devices that provide controlover APs) located in a centralized datacenter 324. For example, accesspoints 320 may communicate with WLCs 326 via a VPN 322 and network datacollection platform 304 may, in turn, communicate with the devices indatacenter 324 to retrieve the corresponding network feature data fromaccess points 320, WLCs 326, etc. In such a centralized model, accesspoints 320 may be flexible access points and WLCs 326 may be N+1 highavailability (HA) WLCs, by way of example.

Conversely, the local network of campus 308 may instead use any numberof access points 328 (e.g., a first access point AP1 through nth accesspoint APm) that provide connectivity to endpoint nodes, in adecentralized manner. Notably, instead of maintaining a centralizeddatacenter, access points 328 may instead be connected to distributedWLCs 330 and switches/routers 332. For example, WLCs 330 may be 1:1 HAWLCs and access points 328 may be local mode access points, in someimplementations.

To support the operations of the network, there may be any number ofnetwork services and control plane functions 310. For example, functions310 may include routing topology and network metric collection functionssuch as, but not limited to, routing protocol exchanges, pathcomputations, monitoring services (e.g., NetFlow or IP Flow InformationExport (IPFIX) exporters), etc. Further examples of functions 310 mayinclude authentication functions, such as by an Identity Services Engine(ISE) or the like, mobility functions such as by a Connected MobileExperiences (CMX) function or the like, management functions, and/orautomation and control functions such as by an APIC-Enterprise Manager(APIC-EM).

During operation, network data collection platform 304 may receive avariety of data feeds that convey collected data 334 from the devices ofbranch office 306 and campus 308, as well as from network services andnetwork control plane functions 310. Example data feeds may comprise,but are not limited to, management information bases (MIBS) with SimpleNetwork Management Protocol (SNMP)v2, JavaScript Object Notation (JSON)Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reportingin order to collect rich datasets related to network control planes(e.g., Wi-Fi roaming, join and authentication, routing, QoS, PHY/MACcounters, links/node failures), traffic characteristics, and other suchtelemetry data regarding the monitored network. As would be appreciated,network data collection platform 304 may receive collected data 334 on apush and/or pull basis, as desired. Network data collection platform 304may prepare and store the collected data 334 for processing by cloudservice 302. In some cases, network data collection platform may alsoanonymize collected data 334 before providing the anonymized data 336 tocloud service 302.

In some cases, cloud service 302 may include a data mapper andnormalizer 314 that receives the collected and/or anonymized data 336from network data collection platform 304. In turn, data mapper andnormalizer 314 may map and normalize the received data into a unifieddata model for further processing by cloud service 302. For example,data mapper and normalizer 314 may extract certain data features fromdata 336 for input and analysis by cloud service 302.

In various embodiments, cloud service 302 may include a machine learning(ML)-based analyzer 312 configured to analyze the mapped and normalizeddata from data mapper and normalizer 314. Generally, analyzer 312 maycomprise a power machine learning-based engine that is able tounderstand the dynamics of the monitored network, as well as to predictbehaviors and user experiences, thereby allowing cloud service 302 toidentify and remediate potential network issues before they happen.

Machine learning-based analyzer 312 may include any number of machinelearning models to perform the techniques herein, such as for cognitiveanalytics, predictive analysis, and/or trending analytics as follows:

-   -   Cognitive Analytics Model(s): The aim of cognitive analytics is        to find behavioral patterns in complex and unstructured        datasets. For the sake of illustration, analyzer 312 may be able        to extract patterns of Wi-Fi roaming in the network and roaming        behaviors (e.g., the “stickiness” of clients to APs 320, 328,        “ping-pong” clients, the number of visited APs 320, 328, roaming        triggers, etc). Analyzer 312 may characterize such patterns by        the nature of the device (e.g., device type, OS) according to        the place in the network, time of day, routing topology, type of        AP/WLC, etc., and potentially correlated with other network        metrics (e.g., application, QoS, etc.). In another example, the        cognitive analytics model(s) may be configured to extract AP/WLC        related patterns such as the number of clients, traffic        throughput as a function of time, number of roaming processed,        or the like, or even end-device related patterns (e.g., roaming        patterns of iPhones, IoT Healthcare devices, etc.).    -   Predictive Analytics Model(s): These model(s) may be configured        to predict user experiences, which is a significant paradigm        shift from reactive approaches to network health. For example,        in a Wi-Fi network, analyzer 312 may be configured to build        predictive models for the joining/roaming time by taking into        account a large plurality of parameters/observations (e.g., RF        variables, time of day, number of clients, traffic load,        DHCP/DNS/Radius time, AP/WLC loads, etc.). From this, analyzer        312 can detect potential network issues before they happen.        Furthermore, should abnormal joining time be predicted by        analyzer 312, cloud service 312 will be able to identify the        major root cause of this predicted condition, thus allowing        cloud service 302 to remedy the situation before it occurs. The        predictive analytics model(s) of analyzer 312 may also be able        to predict other metrics such as the expected throughput for a        client using a specific application. In yet another example, the        predictive analytics model(s) may predict the user experience        for voice/video quality using network variables (e.g., a        predicted user rating of 1-5 stars for a given session, etc.),        as function of the network state. As would be appreciated, this        approach may be far superior to traditional approaches that rely        on a mean opinion score (MOS). In contrast, cloud service 302        may use the predicted user experiences from analyzer 312 to        provide information to a network administrator or architect in        real-time and enable closed loop control over the network by        cloud service 302, accordingly. For example, cloud service 302        may signal to a particular type of endpoint node in branch        office 306 or campus 308 (e.g., an iPhone, an IoT healthcare        device, etc.) that better QoS will be achieved if the device        switches to a different AP 320 or 328.    -   Trending Analytics Model(s): The trending analytics model(s) may        include multivariate models that can predict future states of        the network, thus separating noise from actual network trends.        Such predictions can be used, for example, for purposes of        capacity planning and other “what-if” scenarios.

Machine learning-based analyzer 312 may be specifically tailored for usecases in which machine learning is the only viable approach due to thehigh dimensionality of the dataset and patterns cannot otherwise beunderstood and learned. For example, finding a pattern so as to predictthe actual user experience of a video call, while taking into accountthe nature of the application, video CODEC parameters, the states of thenetwork (e.g., data rate, RF, etc.), the current observed load on thenetwork, destination being reached, etc., is simply impossible usingpredefined rules in a rule-based system.

Unfortunately, there is no one-size-fits-all machine learningmethodology that is capable of solving all, or even most, use cases. Inthe field of machine learning, this is referred to as the “No FreeLunch” theorem. Accordingly, analyzer 312 may rely on a set of machinelearning processes that work in conjunction with one another and, whenassembled, operate as a multi-layered kernel. This allows networkassurance system 300 to operate in real-time and constantly learn andadapt to new network conditions and traffic characteristics. In otherwords, not only can system 300 compute complex patterns in highlydimensional spaces for prediction or behavioral analysis, but system 300may constantly evolve according to the captured data/observations fromthe network.

Cloud service 302 may also include output and visualization interface318 configured to provide sensory data to a network administrator orother user via one or more user interface devices (e.g., an electronicdisplay, a keypad, a speaker, etc.). For example, interface 318 maypresent data indicative of the state of the monitored network, currentor predicted issues in the network (e.g., the violation of a definedrule, etc.), insights or suggestions regarding a given condition orissue in the network, etc. Cloud service 302 may also receive inputparameters from the user via interface 318 that control the operation ofsystem 300 and/or the monitored network itself. For example, interface318 may receive an instruction or other indication to adjust/retrain oneof the models of analyzer 312 from interface 318 (e.g., the user deemsan alert/rule violation as a false positive).

In various embodiments, cloud service 302 may further include anautomation and feedback controller 316 that provides closed-loop controlinstructions 338 back to the various devices in the monitored network.For example, based on the predictions by analyzer 312, the evaluation ofany predefined health status rules by cloud service 302, and/or inputfrom an administrator or other user via input 318, controller 316 mayinstruct an endpoint client device, networking device in branch office306 or campus 308, or a network service or control plane function 310,to adjust its operations (e.g., by signaling an endpoint to use aparticular AP 320 or 328, etc.).

As noted above, one implementation of a network assurance system iscloud-based and entails sending all data to a cloud service foranalysis, potentially after anonymization of sensitive data incompliance with privacy standards, such as the General Data ProtectionRegulation (GDPR) in Europe. In a second approach, also referred to as“on premise,” the machine learning analysis may be performed on a devicehosted on the local network (e.g., in datacenter 324). Notably, someentities may prefer to prevent their data from being sent to the cloud,even with anonymization techniques in place. To implement “on premise”network assurance, the cloud-based models (e.g., of ML-based analyzer312) may be sent to the hosting device in the local network, where localtraining can take place without requiring any uploading of telemetrydata to the cloud.

Unfortunately, on premise implementations are not without drawbacks.First, there are typically fewer resources available at any given sitethan in the cloud for purposes of training complex machine learningmodels. Second, the training data available on the local network may beless diverse than that available to a cloud-based service, which canleverage cross learning using training data from any number of networksmonitored by the service. Third, the amount of local data that can bestored in an on premise implementation is also likely to be more limitedthan that of a cloud-based implementation, thus limiting the scope oftraining data that can be used to train the machine learning model(s).

Dynamic Selection of Models for Hybrid Network Assurance Architectures

The techniques herein allow for a hybrid network assurance architecturewhereby a thin, local/on premise client can still use machine learningmodels to locally assess the network and without requiring the sendingof confidential data to the cloud. In some aspects, the on premise localagent may send a custom request to the cloud service that specifies thelist of use cases of interest for the network. In turn, the cloudservice may return a classifier to the local agent along with a set ofmodeling strategies corresponding to the various labels of theclassifier. In further aspects, the local agent can then use theclassifier to select an appropriate modeling strategy for the networkand, in turn, install a machine learning-based model to analyze thenetwork, in accordance with the selected modeling strategy.

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a local service of a network reportsconfiguration information regarding the network to a cloud-based networkassurance service. The local service receives a classifier selected bythe cloud-based network assurance service based on the configurationinformation regarding the network. The local service classifies, usingthe received classifier, telemetry data collected from the network, toselect a modeling strategy for the network. The local service installs,based on the modeling strategy for the network, a machine learning-basedmodel to the local service for monitoring the network.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with thenetwork assurance process 248, which may include computer executableinstructions executed by the processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein.

Operationally, FIGS. 4A-4F illustrates an example hybrid networkassurance system 400, according to various embodiments. At the core ofarchitecture 400 may be the following primary components: 1.) a localservice 406 that operates on premise of the network to be monitored and2.) a network assurance cloud service 302, as described previously. Thesub-components of these two services may be implemented in a distributedmanner or implemented as their own stand-alone services, in variousembodiments. In addition, the functionalities of the components andsub-components of hybrid network assurance system 400 may be combined,omitted, or implemented as part of other processes, as desired.

More specifically, as shown in FIG. 4A, a local service 406 may beimplemented on premise of the network to be monitored. Accordingly,local service 406 may include the network data collection platform 304described previously, which receives collected data 334 (e.g., telemetrydata, configuration data, etc.) from the network entities 404 in themonitored network that provide connectivity to clients in the network,such as client 402 shown. For example, network entities 404 may include,but are not limited to, APs, WLCs/wireless controllers, switches,routers, and the like. In addition, network data collection platform 304of local service 406 may also control the operation of network entities404 via control commands 338, based on the monitoring.

Typically, local service 406 may include local versions of the variousmodules of cloud service 302. For example, as shown, local service 406may include a local machine learning-based analyzer 312 a, a local datamapper and normalizer 314 a, a local automation and feedback controller316 a, and/or a local output and visualization interface 318 a. Suchmodules may generally offer the same functionality as those of cloudservice 302, but may also be scaled down, in some cases, and offer morelimited functionality (e.g., only certain visualizations, machinelearning models that are only pertinent to the specific network, etc.).In various embodiments, each of services 302, 406 may also include anagent configured to facilitate collaboration between services 302, 406.Notably, local service 406 may include a local agent 408 configured tocommunicate with a cloud agent 410 of cloud service 302.

As noted, reliance on a cloud-based network assurance service allows forcross learning using data sets from any number of different networksmonitored by the cloud-based service. Unfortunately, in many situations,there is not one machine learning-based model that fits all use cases,thereby requiring a separate model for each use case. For example,consider the case of an anomaly detection model that looks forstatistical deviations in the assessed network data (e.g., to detectwhen the behavior of the monitored network is no longer “normal”).Finding anomalies using such a model usually requires differentparameter settings, depending on the use case. In particular, the timewindows assessed by the model, the percentile values used to define whatis anomalous, the type of anomaly detection approach taken, rescalingfactors used by the model, etc., may be a function of the use case(e.g., the configuration of the monitored network, the nature of thenetwork traffic, etc.). It has also been shown that the performance ofsuch a model can drop considerably when applied to a different use case,such as using the model in another network with a very different trafficprofile.

While model selection may typically be performed in the cloud, thisselection is also dependent on data being sent from the monitorednetwork to the cloud for analysis (e.g., after anonymization). However,as noted, this may not be desirable in all circumstances, therebyleading to the use of a hybrid network assurance system, such as system400 shown. In such cases, model selection may instead be performedlocally, on premise, using the information collected from the network.

As shown in FIG. 4B, one aspect of the techniques herein introduces acustom message called a use_case_select( ) message 412 that may be sentby local agent 408 of local service 406 to cloud agent 410 of cloudservice 302. In various embodiments, message 412 may convey to cloudagent 410 information regarding the use case for the local network. Forexample, message 412 may include information regarding any or all of thefollowing:

-   -   The initial configuration of the network during installation        (e.g., which devices are installed, the network layout, etc.).    -   Licensing constraints (e.g., in case different licenses are        required for different use cases).    -   Data constraints (e.g., in case certain data is required for a        particular use case, but is unavailable to the local service).

As shown in FIG. 4C, after receiving message 412, cloud agent 410 ofcloud service 302 may compile a listing of modeling strategies, based onthe use case information included in message 412. In addition, cloudagent 410 may also determine the appropriate list of parameters for themodeling strategies. In some embodiments, cloud agent 410 may, for agiven strategy, even identify a pre-trained/pre-computed model fromanalyzer 312 that satisfies a given use case of interest. For example,assume that the on premise network is configured with a set of wirelessAPs to support conferencing traffic while clients roam throughout thenetwork. In such a case, cloud agent 410 may identify the variousmodeling strategies that could be applicable to this use case, forpurposes of monitoring the network. However, the specific modelingstrategy that is applicable to the network may be a function ofinformation that is restricted from sending to cloud service 302, suchas information regarding actual user traffic patterns, etc.

In various embodiments, as shown in FIG. 4D, cloud agent 410 maygenerate and send a classifier 414 back to local agent 408, based on themodeling strategies and parameters identified by cloud agent 410. Ingeneral, classifier 414 may be configured to take as input a set ofinput features (e.g., measurements/characteristics of the network oflocal service 406) and, based on a classification of these features,output a label that corresponds to a particular modeling strategy. Thus,while cloud service 302 is unable to directly select and install theappropriate model to local service 406 for monitoring the on premisenetwork, it can send a classifier to service 406 that enables service406 to determine what would be the most appropriate model for themonitoring.

In response to receiving classifier 414, local agent 408 of localservice 406 may perform any or all of the following:

-   -   Evaluate the classifier using locally collected data from the        network (e.g., data 334), which will provide an output O    -   Select the modeling strategy with classifier output O and:        -   If the modeling strategy corresponds to a set of learning            parameters, perform a learning task on premise with the            suggested parameters, which will produce a model M        -   If the configuration corresponds to a pre-computed model            (e.g., trained by cloud service 302 and provided to local            agent 408 with classifier 414), take the pre-computed model            directly as the model M    -   Evaluate model M for the use case of interest

More specifically, as shown in FIG. 4E, local agent 408 may interactwith network data collection platform 304, to classify collected data334 using classifier 414. In greater detail, local agent 408 may collecttelemetry traffic data from network entities 404 (e.g. Netflow records,IPFIX records, etc.), other network characteristics, such as SimpleNetwork Management Protocol (SNMP) information, Cisco Fusioninformation, etc., or any other information that can be used as inputfeatures for classifier 414. By classifying this data, classifier 414can then output a label that indicates the modeling strategy that localservice 406 should use to model and monitor the network.

By way of example, consider the case of a machine learning-basedregression model that is to be used by analyzer 312 a to predict thenumber of users that will have a bad roaming experience in the wirelessnetwork. In this case, the specific modeling strategy may vary dependingon the type of APs deployed in the network, the traffic profile in thenetwork, and/or other network characteristics that can significantlyaffect what regression model should be used in the network. In thiscase, classifier 414 aids in the selection process by taking intoaccount a set of input feature characteristics of the network (e.g., thetype of networking gear deployed in the network, a sample of the networktraffic, configuration of the networking gear, etc.). For the sake ofillustration, classifier 414 may even take as input feature the OSrelease used locally. Indeed, it has been shown that some modelingstrategies may be specific to the OS release. In such a case, localagent 408 can determine which modeling strategy to use, without havingto send this information to cloud service 302.

In one embodiment the list of candidate input features for classifier414 is provided by local agent 408. In another embodiment, cloud agent410 may provide the set of input features required by classifier 414along with the classifier itself. In a further embodiment, an additionalmechanism can be used to handle the case where the set of input featuresrequired by classifier 414 to select the appropriate modeling strategyis not locally available on local agent 408 (e.g., local agent 408 maynot have access to the configuration of the switch that is used by theML model). In this case, local agent 408 may provide the list of locallyavailable classifier input features to cloud agent 410 (e.g., inconjunction with message 412). On receiving the list of availablefeatures, cloud agent 410 may trigger the computation of a customclassifier 414 using only the set of available features. Continuing theexample whereby local agent 408 cannot access the configuration of agiven switch, it may still be possible to compute a classifier 414 thatdoes not require this input to select a modeling strategy. Note that themodeling strategy selection is performed by local agent 408 executingclassifier 414, without requiring it to send any confidential data tocloud service 302.

As shown in FIG. 4F, once local agent 408 has selected the appropriatemodeling strategy, it may install the corresponding machinelearning-based model to local analyzer 312 a, to begin monitoring thenetwork. As noted, in some cases, cloud agent 410 may send a pre-trainedmodel in conjunction with classifier 414. In such a case, cloud agent410 may simply install this model to analyzer 312 a, when the output ofclassifier 414 indicate that this model should be used. In furtherembodiments, however, the modeling strategy selected by classifier 414may indicate how local agent 408 should train the model usinginformation collected from the monitored network. Once trained, localagent 408 may then install the model to local analyzer 412 a, in asimilar manner.

Since the local conditions of the network are subject to change overtime, local agent 408 may re-perform the above functions afterexpiration of a configurable timer, in some embodiments. For example,after expiration of the timer, local agent 408 may re-run classifier 414on more current information from the network and, if the classificationresults have changed, select and install a new model to analyzer 312 a.The major caveat, though, is that, this can result in the loss ofhistory used by the model. For example, retraining a new anomalydetection model from scratch may result in a loss of the prior notion ofwhat is considered “normal” in the network (e.g., by changing whichnetwork characteristics are assessed by the anomaly detector, etc.).

In yet another embodiment, local agent 408 may use an anomaly detectionprocess on the input features for classifier 414, itself, to determinewhether the profile of the local network has changed. Indeed, if cloudagent 410 has provided a classifier 414 that assesses n-number offeatures F₁, . . . , F_(n) to select the appropriate modeling strategyfor the network, such variables are likely to greatly influence theselection process. Thus, local agent 408 may specifically monitorchanges in these features and, on detecting significant changes, localagent 408 may even request a new classifier from cloud service 302.

FIG. 5 illustrates an example simplified procedure for model selectionin a hybrid network assurance system in a network in accordance with oneor more embodiments described herein. For example, a non-generic,specifically configured device (e.g., device 200) that implements alocal service in a network may perform procedure 500 by executing storedinstructions (e.g., process 248). The procedure 500 may start at step505, and continues to step 510, where, as described in greater detailabove, the local service may report configuration information regardingthe network to a cloud-based network assurance service. Theconfiguration information may indicate, for example, the networkingequipment installed in the network (e.g., APs, wireless controllers,switches, etc.), the layout of the equipment, the number of users in thenetwork, or any other configuration information regarding the network.Further information that the local service may report can include dataconstraint information, such as when certain information is notavailable to the local service regarding the network. In further cases,the local service may also report license information to the cloud-basedservice.

At step 515, as detailed above, the local service may receive aclassifier selected by the cloud-based network assurance service basedon the configuration information regarding the network. In general, theclassifier may be configured to take as input any number of inputfeatures (e.g., characteristics of the network, such as telemetry data,configuration information, etc.) and output a label corresponding to amodeling strategy. In some cases, the modeling strategy may simplyspecify a pre-trained model from the cloud service for installation tothe local service to monitor the network. In further cases, the modelingstrategy may specify how the local service should train a model forinstallation to the local service to monitor the network.

At step 520, the local service may use the classifier to classifytelemetry data collected from the network, to select a modeling strategyfor the network, as described in greater detail above. For example, thelocal service may classify Netflow, IPFIX, or any other form oftelemetry data from the network. In further cases, the local service mayalso classify information such as SNMP data, or the like, from thenetwork.

At step 525, as detailed above, the local service may install, based onthe modeling strategy for the network, a machine learning-based model tothe local service for monitoring the network. For example, the localservice may install a model trained by the cloud-based service ortrained locally for monitoring the network. The local service may thenuse the model to monitor the network and, based on the monitoring,control operation of the network (e.g., by moving a client to adifferent AP, changing a routing path or data rate, etc.). Procedure 500then ends at step 530.

It should be noted that while certain steps within procedure 500 may beoptional as described above, the steps shown in FIG. 5 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, allow for the localselection of models for purposes of performing local networkassurance/monitoring, without requiring the local network assuranceservice to send sensitive information regarding the network to thecloud.

While there have been shown and described illustrative embodiments thatprovide for model selection in a network assurance system, it is to beunderstood that various other adaptations and modifications may be madewithin the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein with respect to usingcertain models for purposes of anomaly detection or network monitoring,the models are not limited as such and may be used for other functions,in other embodiments. In addition, while certain protocols are shown,such as SNMP, other suitable protocols may be used, accordingly.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method comprising: reporting, by a local service of a network, configuration information regarding the network to a cloud-based network assurance service; receiving, at the local service, a classifier generated by the cloud-based network assurance service based on a plurality of modeling strategies that are selected by the cloud-based network assurance service according to the configuration information reported by the local service; classifying, by the local service and using the received classifier, telemetry data collected from the network to select a modeling strategy for the network among the plurality of modeling strategies selected by the cloud-based network assurance service; and installing, by the local service and based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network.
 2. The method as in claim 1, wherein the installed machine learning-based model is a machine learning-based anomaly detector or a trained machine learning-based classifier that assesses traffic information from the network.
 3. The method as in claim 1, further comprising: reporting, by the local service and to the cloud-based network assurance service, a set of potential input features for the classifier that are available to the local service.
 4. The method as in claim 1, further comprising: receiving, at the local service and from the cloud-based network assurance service, the machine learning-based model, wherein the cloud-based network assurance service trained the model, and wherein the modeling strategy specifies the trained model for installation.
 5. The method as in claim 1, wherein installing, by the local service and based on the modeling strategy for the network, the machine learning-based model to the local service for monitoring the network comprises: training, by the local service, the machine learning-based model according to the modeling strategy, wherein the modeling strategy is determined by an output of the received classifier.
 6. The method as in claim 5, wherein the modeling strategy specifies at least one of: a time window, percentile value, type of anomaly detection, or rescaling factor to be used by the machine learning-based model.
 7. The method as in claim 1, further comprising: detecting, by the local service, a change in input features from the network for the classifier; and, in response, requesting, by the local service, a new classifier from the cloud-based network assurance service.
 8. The method as in claim 1, further comprising: identifying, by the local service, expiration of a reporting timer, wherein the local service reports the configuration information regarding the network to the cloud-based network assurance service after expiration of the reporting timer.
 9. The method as in claim 1, wherein classifying the telemetry data collected from the network, to select a modeling strategy for the network, further comprises: classifying, using the received classifier, Simple Network Management Protocol (SNMP), Internet Protocol Flow Information Export (IPFIX), or Netflow information from the network.
 10. The method as in claim 1, further comprising: using, by the local service, the installed machine learning-based model to monitor the network; and controlling, by the local service, operation of the network based on the monitoring.
 11. An apparatus, comprising: one or more network interfaces to communicate with a network; a processor coupled to the network interfaces and configured to execute one or more processes; and a memory configured to store a process executable by the processor, the process when executed configured to: report configuration information regarding the network to a cloud-based network assurance service; receive a classifier generated by the cloud-based network assurance service based on a plurality of modeling strategies that are selected by the cloud-based network assurance service according to the configuration information reported by the local service; classify, using the received classifier, telemetry data collected from the network to select a modeling strategy for the network among the plurality of modeling strategies selected by the cloud-based network assurance service; and install, based on the modeling strategy for the network, a machine learning-based model for monitoring the network.
 12. The apparatus as in claim 11, wherein the installed machine learning-based model is a machine learning-based anomaly detector or a trained machine learning-based classifier that assesses traffic information from the network.
 13. The apparatus as in claim 11, wherein the apparatus receives the classifier selected by the cloud-based network assurance service by: receiving, from the cloud-based network assurance service, a set of input features required by the classifier.
 14. The apparatus as in claim 11, wherein the process when executed is further configured to: receive, from the cloud-based network assurance service, the machine learning-based model, wherein the cloud-based network assurance service trained the model, and wherein the modeling strategy specifies the trained model for installation.
 15. The apparatus as in claim 11, wherein the apparatus installs, based on the modeling strategy for the network, the machine learning-based model for monitoring the network by: training the machine learning-based model according to the modeling strategy, wherein the modeling strategy is determined by an output of the received classifier.
 16. The apparatus as in claim 15, wherein the modeling strategy specifies at least one of: a time window, percentile value, type of anomaly detection, or rescaling factor to be used by the machine learning-based model.
 17. The apparatus as in claim 11, wherein the process when executed is further configured to: detect a change in input features from the network for the classifier; and, in response, request a new classifier from the cloud-based network assurance service.
 18. The apparatus as in claim 11, wherein the process when executed is further configured to: identify an expiration of a reporting timer, wherein the configuration information regarding the network is reported to the cloud-based network assurance service after expiration of the reporting timer.
 19. The apparatus as in claim 11, wherein the apparatus classifies the telemetry data collected from the network, to select a modeling strategy for the network, by: classifying, using the received classifier, Simple Network Management Protocol (SNMP), Internet Protocol Flow Information Export (IPFIX), or Netflow information from the network.
 20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a local service in a network to execute a process comprising: reporting, by the local service of the network, configuration information regarding the network to a cloud-based network assurance service; receiving, at the local service, a generated selected by the cloud-based network assurance service based on a plurality of modeling strategies that are selected by the cloud-based network assurance service according to the configuration information reported by the local service; classifying, by the local service and using the received classifier, telemetry data collected from the network to select a modeling strategy for the network among the plurality of modeling strategies selected by the cloud-based network assurance service; and installing, by the local service and based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network. 